Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy

Ogawa, Takeshi; Hirayama, Jun-ichiro; Gupta, Pankaj; Moriya, Hideshige; Yamaguchi, Shumpei; Ishikawa, Atsushi; Inoué, Yoshihiro; Kawanabe, Motoaki; Ishii, Shin

doi:10.1109/embc.2015.7318559

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…All experiments were conducted in the experimental smart house [8]. The participants sat on a comfortable chair at a distance of 1.2m from a television (Regza, Toshiba Co., Tokyo, Japan).…”

Section: A Experimental Procedures and Data Acquisitionmentioning

confidence: 99%

Decoding emotional valence from electroencephalographic rhythmic activity

Çelikkanat

Moriya

Ogawa

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.

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“…All experiments were conducted in the experimental smart house [8]. The participants sat on a comfortable chair at a distance of 1.2m from a television (Regza, Toshiba Co., Tokyo, Japan).…”

Section: A Experimental Procedures and Data Acquisitionmentioning

confidence: 99%

Decoding emotional valence from electroencephalographic rhythmic activity

Çelikkanat

Moriya

Ogawa

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

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“…In this review, a BCI system is classified as a good system when the EEG signal processing algorithm removes artifacts according to the dynamic approach which leads to a quasi-stationary system accuracy for all subjects. According to the previous law, the system presented in Reference [ 29 ] is classified as a good because the accuracy is almost the same for all users. On the contrary, the system presented in Reference [ 30 ] is classified as a bad system due to the accuracy fluctuation from subject to another.…”

Section: Introductionmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

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“…In this study, our primary focus is on four physiological signals -electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR). Numerous studies use EEG for applications such as assistive smart homes [7], seizure detection [8], driver drowsiness [9], classification of autism [10] and mental stress detection. EEG can capture localized signals of the brain regions that generate the stress response and can give many robust features for assessing stress [11].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based stress classification system using wearable sensor devices

Chandra,

Sethia

2024

IJ-AI

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<p>University students often become victims of high-stress levels due to the highly competitive work environment. Unmonitored stress levels in students can inflict severe physiological health problems. This work aims to build a stress classification framework using wearable sensor devices to predict mental stress levels for undergraduate engineering students. It comprises a study to collect a data set of 23 university students using wearable devices for four physiological signals, i.e., electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR), when the students perform the montreal imaging stress task (MIST) for the mental workload. The machine learning models proposed in this work help classify stress into three levels: rest, moderate, and high. The models achieve a classification accuracy of 99.98% using the EEG signals’ time-frequency domain features and an accuracy of 99.51% using the EDA, HR, and SKT signals. The proposed models achieve better scores than all the previous studies on stress classification, using EEG signals and EDA, HR, and SKT signals. This study is novel since it also demonstrates the applicability and proficiency of wearable sensor devices in developing accurate stress classification models to help build real-time stress monitoring systems.</p>

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Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy

Cited by 10 publications

References 13 publications

Decoding emotional valence from electroencephalographic rhythmic activity

Decoding emotional valence from electroencephalographic rhythmic activity

Embedded Brain Computer Interface: State-of-the-Art in Research

Machine learning-based stress classification system using wearable sensor devices

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